Controllable and efficient diffusion models
NAVER LABS Europe seminars are open to the public. Please register to participate.
Date: 20th January 2026, 10:00 am (CET)
About the speaker: Dimitris Samaras graduated in computer science and engineering at the University of Patras in 1992. He obtained his MSc in 1994 from Northeastern University in 1994 and his Ph.D. in 2001 at the University of Pennsylvania. He is a SUNY Empire Innovation Professor of Computer Science with Stony Brook University, where he directs the Computer Vision Lab. His research interests include human behavior analysis, generative models, illumination modeling and estimation for recognition and graphics, and biomedical image analysis. He has co-authored over 200 peer-reviewed research articles in Computer Vision, Machine Learning, Computer Graphics and Medical Imaging conferences and journals. He was Program Chair of CVPR 2022, General Chair of ACCV 2024 and ICCV 2025, and is frequent Area Chair in Computer Vision and Machine Learning conferences.
Abstract: State-of-the-art diffusion models can generate visually compelling images and videos, but they require copious amounts of annotated training data and compute time, available. At the same time controlling the output to particular conditions, descriptions, scene and global context etc. without extensive retraining, becomes very important as visual generation applications become democratized. In this talk I will go over our group’s efforts to make diffusion models more controllable and efficient. First, Ι will discuss how to align video diffusion models more closely semantically with the input controls and also produce content that more accurately follows physics constraints using post-training. Then, I will introduce a method that guides diffusion models to generate fewer semantically implausible samples (hallucinations) at inference time. Finally, I will review our line of work that uses self-supervised representations as conditioning signals for diffusion models in digital histopathology for large image generation, to provide better, more direct control over the generated images. Such large image generation has led us to introduce an algorithm for test-time, constrained sampling that does not require any backpropagation and thus more efficiently produces results as good as previous optimization methods. Finally, I will talk about two ideas on optimizing the inference of large image and video diffusion models for efficiency: how to focus computation in important regions only by merging tokens efficiently while preserving quality and how to effectively perform mixed-resolution inference to decrease inference time by appropriately adapting RoPE positional embeddings.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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